package com.xiaoxu.logProject.App

import com.xiaoxu.logProject.dao.StatDAO
import com.xiaoxu.logProject.entity.{DayCityVideoAccessStat, DayVideoAccessStat, DayVideoTrafficsStat}
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._
import org.apache.spark.sql.{DataFrame, SparkSession, expressions}

import scala.collection.mutable.ListBuffer

object TopNStatApp {
  def main(args: Array[String]) {
    val spark = SparkSession.builder()
      .appName("TopNStatJob")
      .config("spark.sql.sources.partitionColumnTypeInference.enabled", "false") //关闭数据格式推测
      .master("local[2]")
      .getOrCreate()

    val sourcePath = "data/output/SparkStatCleanApp/parquet"

    //url cmsType cmsId traffic ip city time day
    val accessDF =
      spark
        .read
        .format("parquet")
        .load(sourcePath)

    accessDF.printSchema()

    //accessDF.show(false)

    val day = "20180511"
    //StatDAO.deleteData(day)

    //最受欢迎的TopN课程
    //videoAccessTopNStat(spark, accessDF, day)

    //按照地市进行统计TopN课程
    //cityAccessTopNStat(spark, accessDF, day)

    //按照流量进行统计
    videoTrafficsTopNStat(spark, accessDF, day)

    spark.stop()
  }

  /**
    * 按照流量进行统计
    */
  def videoTrafficsTopNStat(spark: SparkSession, accessDF: DataFrame, day: String): Unit = {
    //需要导入隐式转换解决 filter($"day" === day && $"cmsType" === "video")报错
    import spark.implicits._

    val cityAccessTopNDF = accessDF
      .filter($"day" === day && $"cmsType" === "video")
      .groupBy("day", "cmsId")
      .agg(sum("traffic")
        .as("traffics"))
      .orderBy($"traffics".desc)

      cityAccessTopNDF.show(false)

    /**
          +--------+-----+--------+
      |day     |cmsId|traffics|
      +--------+-----+--------+
      |20180511|14540|1462179 |
      |20180511|14390|742896  |
      |20180511|4000 |739658  |
      |20180511|4600 |735648  |
      |20180511|4500 |726454  |
      |20180511|14704|719389  |
      |20180511|14623|697955  |
      |20180511|14322|693780  |
      +--------+-----+--------+

      */




    /**
      * 将统计结果写入到MySQL中
      */
    try {
      cityAccessTopNDF.foreachPartition(partitionOfRecords => {

        /**partitionOfRecords
          * |20180511|14540|1462179 |
          * |20180511|14390|742896  |
          * |20180511|4000 |739658  |
          */

        val list = new ListBuffer[DayVideoTrafficsStat]

        partitionOfRecords.foreach(info => {
          //info |20180511|14540|1462179 |
          val day = info.getAs[String]("day")
          val cmsId = info.getAs[Long]("cmsId")
          val traffics = info.getAs[Long]("traffics")
          val entity = DayVideoTrafficsStat(day, cmsId, traffics)
          list.append(entity)
        })

        StatDAO.insertDayVideoTrafficsAccessTopN(list)
      })
    } catch {
      case e: Exception => e.printStackTrace()
    }

  }


  /**
    * 按照地市进行统计TopN课程
    */
  def cityAccessTopNStat(spark: SparkSession, accessDF: DataFrame, day: String): Unit = {
    import spark.implicits._

    val cityAccessTopNDF =
      accessDF
        .filter($"day" === day && $"cmsType" === "video")
        .groupBy("day", "city", "cmsId")
        .agg(
          count("cmsId")
        )

    //cityAccessTopNDF.show(false)

    //Window函数在Spark SQL的使用
    val top3DF = cityAccessTopNDF.select(
      cityAccessTopNDF("day"),
      cityAccessTopNDF("city"),
      cityAccessTopNDF("cmsId"),
      cityAccessTopNDF("times"),
      row_number()
        .over(Window.partitionBy(cityAccessTopNDF("city"))
          .orderBy(cityAccessTopNDF("times").desc))
        .as("times_rank")


    )
      .filter("times_rank <=3")

    top3DF.show(false) //Top3


    /**
      * 将统计结果写入到MySQL中
      */
    try {
      top3DF.foreachPartition(partitionOfRecords => {
        val list = new ListBuffer[DayCityVideoAccessStat]

        partitionOfRecords.foreach(info => {
          val day = info.getAs[String]("day")
          val cmsId = info.getAs[Long]("cmsId")
          val city = info.getAs[String]("city")
          val times = info.getAs[Long]("times")
          val timesRank = info.getAs[Int]("times_rank")
          list.append(DayCityVideoAccessStat(day, cmsId, city, times, timesRank))
        })

        StatDAO.insertDayCityVideoAccessTopN(list)
      })
    } catch {
      case e: Exception => e.printStackTrace()
    }

  }


  /**
    * 最受欢迎的TopN课程
    */
  def videoAccessTopNStat(spark: SparkSession, accessDF: DataFrame, day: String): Unit = {

    /**
      * 使用DataFrame的方式进行统计
      */
    import spark.implicits._

    val videoAccessTopNDF =
      accessDF
        .filter($"day" === day && $"cmsType" === "video")
        .groupBy("day", "cmsId")
        .agg(count("cmsId")
          .as("times"))
        .orderBy($"times".desc)

    //videoAccessTopNDF.show(false)

    /**
      * 使用SQL的方式进行统计
      */
    //    accessDF.createOrReplaceTempView("access_logs")
    //    val videoAccessTopNDF = spark.sql("select day,cmsId, count(1) as times from access_logs " +
    //      "where day='20170511' and cmsType='video' " +
    //      "group by day,cmsId order by times desc")
    //
    //    videoAccessTopNDF.show(false)

    /**
      * 将统计结果写入到MySQL中
      */
    try {
      videoAccessTopNDF.foreachPartition(partitionOfRecords => {
        val list = new ListBuffer[DayVideoAccessStat]

        partitionOfRecords.foreach(info => {
          val day = info.getAs[String]("day")
          val cmsId = info.getAs[Long]("cmsId")
          val times = info.getAs[Long]("times")

          /**
            * 不建议大家在此处进行数据库的数据插入
            */

          list.append(DayVideoAccessStat(day, cmsId, times))
        })

        StatDAO.insertDayVideoAccessTopN(list)
      })
    } catch {
      case e: Exception => e.printStackTrace()
    }

  }
}
